Actions You Can Handle: Dependent Types for AI Plans

TL;DR

This paper introduces a methodology to embed AI plans into dependently-typed language Agda, enabling advanced reasoning and verification of plans beyond traditional AI planner capabilities.

Contribution

It proposes embedding AI plans into Agda, allowing for more expressive verification and reasoning about plans with dependent types.

Findings

Enables reasoning about general properties of AI plans.

Provides a holistic language infrastructure for plan execution.

Extends the expressiveness of AI planning verification.

Abstract

Verification of AI is a challenge that has engineering, algorithmic and programming language components. For example, AI planners are deployed to model actions of autonomous agents. They comprise a number of searching algorithms that, given a set of specified properties, find a sequence of actions that satisfy these properties. Although AI planners are mature tools from the algorithmic and engineering points of view, they have limitations as programming languages. Decidable and efficient automated search entails restrictions on the syntax of the language, prohibiting use of higher-order properties or recursion. This paper proposes a methodology for embedding plans produced by AI planners into dependently-typed language Agda, which enables users to reason about and verify more general and abstract properties of plans, and also provides a more holistic programming language infrastructure for modelling plan execution.